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Testing the Volatility Term Structure Using Option Hedging Criteria

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  • Robert F. Engle
  • Joshua Rosenberg

Abstract

The volatility term structure (VTS) reflects market expectations of asset volatility over different horizons. These expectations change over time, giving dynamic structure to the VTS. This paper evaluates volatility models on the basis of their performance in hedging option price changes due to shifts in the VTS. An innovative feature of the hedging approach is its increased sensitivity to several important forms of model misspecification relative to previous testing methods. Volatility hedge parameters are derived for several volatility models incorporating different predicted VTS dynamics and information variables. Hedging tests using S&P500 index options indicate that the GARCH components with leverage VTS estimate is most accurate. Evidence is obtained for mean-reversion in volatility and correlation between VTS shifts and S&P500 returns. While a convexity hedge dominates the volatility hedges for the observed sample, this result appears to be due to sample selection bias.
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Suggested Citation

  • Robert F. Engle & Joshua Rosenberg, 1966. "Testing the Volatility Term Structure Using Option Hedging Criteria," New York University, Leonard N. Stern School Finance Department Working Paper Seires 96-24, New York University, Leonard N. Stern School of Business-.
    • Handle: RePEc:fth:nystfi:96-24
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    Cited by:

    1. is not listed on IDEAS
    2. Christopher J. Neely & Drew B. Winters, 2005. "Year-end seasonality in one-month LIBOR derivatives," Working Papers 2003-040, Federal Reserve Bank of St. Louis.
    3. Bronka Rzepkowski, 2003. "Order Flows, Delta Hedging and Exchange Rate Dynamics," Working Papers 2003-18, CEPII research center.
    4. Neely, Christopher J., 2009. "Forecasting foreign exchange volatility: Why is implied volatility biased and inefficient? And does it matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(1), pages 188-205, February.
    5. Tobias Adrian & Erkko Etula, 2010. "Funding liquidity risk and the cross-section of stock returns," Staff Reports 464, Federal Reserve Bank of New York.
    6. Christopher J. Neely, 2004. "Implied volatility from options on gold futures: do statistical forecasts add value or simply paint the lilly?," Working Papers 2003-018, Federal Reserve Bank of St. Louis.
    7. Alexandru Badescu & Robert J. Elliott & Juan-Pablo Ortega, 2012. "Quadratic hedging schemes for non-Gaussian GARCH models," Papers 1209.5976, arXiv.org, revised Dec 2013.
    8. Tobias Adrian & Joshua Rosenberg, 2008. "Stock Returns and Volatility: Pricing the Short‐Run and Long‐Run Components of Market Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2997-3030, December.
    9. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    10. Francis X. Diebold, 2004. "The Nobel Memorial Prize for Robert F. Engle," Scandinavian Journal of Economics, Wiley Blackwell, vol. 106(2), pages 165-185, June.
    11. Bentes, Sonia R & Menezes, Rui, 2012. "On the predictive power of implied volatility indexes: A comparative analysis with GARCH forecasted volatility," MPRA Paper 42193, University Library of Munich, Germany.
    12. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
    13. Fengler, Matthias R. & Härdle, Wolfgang & Mammen, Enno, 2003. "Implied volatility string dynamics," SFB 373 Discussion Papers 2003,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    14. Pilar Corredor-Casado & Rafael Santamaría-Aquilué, 2000. "La estructura temporal de las volatilidades implícitas en la opción sobre el IBEX-35," Investigaciones Economicas, Fundación SEPI, vol. 24(2), pages 385-417, May.

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